Analysis by GenomicSuperSignature
2021-12-08
Contents
1 RAVs best represents your dataset
The validation provides a quantitative representation of the relevance
between your dataset and RAVs. Below shows the top 6 validated RAVs and
the complete result is saved as {input_name}_validate.csv.
## score PC sw cl_size cl_num
## RAV1076 0.15262099 2 -0.04447124 10 1076
## RAV725 0.24823617 2 0.09412734 20 725
## RAV884 0.22061781 2 0.15328698 6 884
## RAV1994 0.09894967 4 -0.03493900 3 19941.1 Heatmap Table
heatmapTable takes validation results as its input and displays them into
a two panel table: the top panel shows the average silhouette width (avg.sw)
and the bottom panel displays the validation score.
heatmapTable can display different subsets of the validation output. For
example, if you specify scoreCutoff, any validation result above that score
will be shown. If you specify the number (n) of top validation results through
num.out, the output will be a n-columned heatmap table. You can also use the
average silhouette width (swCutoff), the size of cluster (clsizecutoff),
one of the top 8 PCs from the dataset (whichPC).
Here, we print out top 3 validated RAVs with average silhouette width above 0.
1.2 Interactive Graph
Under the default condition, plotValidate plots validation results of all non
single-element RAVs in one graph, where x-axis represents average silhouette
width of the RAVs (a quality control measure of RAVs) and y-axis represents
validation score. We recommend users to focus on RAVs with higher validation
score and use average silhouette width as a secondary criteria.
Note that interactive = TRUE will result in a zoomable, interactive plot that
included tooltips, which is saved as {input_name}_validate_plot.html file.
You can hover each data point for more information:
- sw : the average silhouette width of the cluster
- score : the top validation score between 8 PCs of the dataset and RAVs
- cl_size : the size of RAVs, represented by the dot size
- cl_num : the RAV number. You need this index to find more information
about the RAV.
- PC : test dataset’s PC number that validates the given RAV. Because we used top 8 PCs of the test dataset for validation, there are 8 categories.
If you double-click the PC legend on the right, you will enter an individual display mode where you can add an additional group of data point by single-click.
2 Prior information associated to your dataset
2.1 MeSH terms in wordcloud
## [1] "MeSH terms related to RAV725"
## [1] "MeSH terms related to RAV884"
2.2 GSEA
The complete result is saved as {input_name}_genesets_RAV*.csv.
## $`Enriched gene sets for RAV725`
## Description NES pvalue
## DMAP_ERY3 DMAP_ERY3 -1.622639 1e-10
## DMAP_ERY4 DMAP_ERY4 -1.613924 1e-10
## DMAP_ERY5 DMAP_ERY5 -1.607208 1e-10
## KEGG_ALZHEIMERS_DISEASE KEGG_ALZHEIMERS_DISEASE -1.881862 1e-10
## KEGG_HUNTINGTONS_DISEASE KEGG_HUNTINGTONS_DISEASE -1.999761 1e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION KEGG_OXIDATIVE_PHOSPHORYLATION -2.300524 1e-10
## qvalues
## DMAP_ERY3 3.251166e-10
## DMAP_ERY4 3.251166e-10
## DMAP_ERY5 3.251166e-10
## KEGG_ALZHEIMERS_DISEASE 3.251166e-10
## KEGG_HUNTINGTONS_DISEASE 3.251166e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION 3.251166e-10
##
## $`Enriched gene sets for RAV884`
## Description NES pvalue
## DMAP_ERY3 DMAP_ERY3 -1.432912 1e-10
## KEGG_ALZHEIMERS_DISEASE KEGG_ALZHEIMERS_DISEASE -1.621256 1e-10
## KEGG_CELL_CYCLE KEGG_CELL_CYCLE -1.651643 1e-10
## KEGG_HUNTINGTONS_DISEASE KEGG_HUNTINGTONS_DISEASE -1.644625 1e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION KEGG_OXIDATIVE_PHOSPHORYLATION -1.809433 1e-10
## KEGG_PARKINSONS_DISEASE KEGG_PARKINSONS_DISEASE -1.840273 1e-10
## qvalues
## DMAP_ERY3 6.025408e-10
## KEGG_ALZHEIMERS_DISEASE 6.025408e-10
## KEGG_CELL_CYCLE 6.025408e-10
## KEGG_HUNTINGTONS_DISEASE 6.025408e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION 6.025408e-10
## KEGG_PARKINSONS_DISEASE 6.025408e-102.3 Publication
The complete result is saved as {input_name}_literatures_RAV*.csv.
## $`Studies related to RAV725`
## studyName
## 534 ERP114425
## 1030 SRP028155
## 1643 SRP049599
## 2691 SRP071854
## 2705 SRP072038
## 2755 SRP072875
## title
## 534 Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters.
## 1030 Transcriptomic analysis of ERR alpha orphan nuclear receptor
## 1643 JunB control of keratinocyte-mediated inflammation [RNA-seq]
## 2691 Parental allele specific single-cell transcriptome dynamics reveal incomplete epigenetic reprogramming in human female germ cells
## 2705 Digitalis-like compounds facilitate redifferentiation of non-medullary thyroid cancer through intracellular Ca2+, cFOS and autophagy dependent pathways
## 2755 Single-nucleus RNA-seq on undifferentiated human KD3 myoblasts and differentiated myotubes and mononucleated cells.
##
## $`Studies related to RAV884`
## studyName
## 806 SRP015640
## 4066 SRP106788
## 4587 SRP119800
## 4990 SRP132313
## 5393 SRP149535
## 5664 SRP156532
## title
## 806 Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples
## 4066 A practical solution for preserving single cells for RNA sequencing
## 4587 Gene expressions in nucleus and cytoplasm at the single cell resolution
## 4990 Multi-platform single cell transcriptomic profiling as a benchmarking resource
## 5393 Human lineage tracing enabled by mitochondrial mutations and single cell genomics [TF1_clones_scRNA]
## 5664 Human lineage tracing enabled by mitochondrial mutations and single cell genomics [TF1_barcoding_scRNA]